Resource Allocation for Video Transcoding in the Multimedia Cloud

Abstract

Video content providers like YouTube, Netix cater their content, i.e. news, shows, on the web which is accessible anytime anywhere. The multi-screens like TVs, smartphones, laptops created a demand to transcode the video into the appropriate video speciation ensuring different Quality of Services (QoS) such as delay. Transcoding a large, high-definition video requires a lot of time, computation. The cloud transcoding solution allows video service providers to overcome the above difficulties through the pay-as-you-use scheme, with the assurance of providing online support to handle unpredictable demands. This paper presents a cost-efficient cloud-based transcoding framework and algorithm (CVS) for streaming service providers. The dynamic resource provisioning policy used in framework finds the number of virtual ma-chines required for a particular set of video streams. Simulation results based on YouTube dataset show that the CVS algorithm performs better compared to FCFS scheme.